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dc.contributor.authorLiu, Lilien
dc.contributor.authorWang, Dingweien
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2013-06-13T12:56:35Z
dc.date.available2013-06-13T12:56:35Z
dc.date.issued2009
dc.identifier.citationLiu, L., Wang, D. and Yang, S. (2009) An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems. In: Applications of evolutionary computing: proceedings of EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Tübingen, Germany, April 15-17, 2009. Berlin: Springer-Verlag, pp. 725-734.en
dc.identifier.isbn978-3-642-01128-3
dc.identifier.urihttp://hdl.handle.net/2086/8734
dc.description.abstractIn recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This paper proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman problems. Within the proposed algorithm, a permutation-based dualism is introduced in the course of clone process to promote the population diversity. In addition, a memory-based vaccination scheme is presented to further improve its tracking ability in dynamic environments. The experimental results show that the proposed diversification and memory enhancement methods can greatly improve the adaptability of genetic algorithms for dynamic traveling salesman problems.en
dc.language.isoenen
dc.publisherSpringer-Verlagen
dc.relation.ispartofseriesLecture Notes in Computer Science;Vol. 5484
dc.titleAn immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-642-01129-0_82
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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